Supervised sentiment analysis in multilingual environments. Issue 3 (May 2017)

• Record Type: Journal Article
• Authors: Vilares, David; Alonso, Miguel A.; Gómez-Rodríguez, Carlos
• Is Part Of: Information processing & management. Volume 53:Issue 3(2017:May)
• Page Start: 595
• Page End: 607
• Publication Date: 2017-05
• Subjects: Sentiment analysis -- Multilingual -- Code-Switching; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2017.01.004
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: This article tackles the problem of performing multilingual polarity classification on Twitter, comparing three techniques: (1) a multilingual model trained on a multilingual dataset, obtained by fusing existing monolingual resources, that does not need any language recognition step, (2) a dual monolingual model with perfect language detection on monolingual texts and (3) a monolingual model that acts based on the decision provided by a language identification tool. The techniques were evaluated on monolingual, synthetic multilingual and code-switching corpora of English and Spanish tweets. In the latter case we introduce the first code-switching Twitter corpus with sentiment labels. The samples are labelled according to two well-known criteria used for this purpose: the SentiStrength scale and a trinary scale ( positive, neutral and negative categories). The experimental results show the robustness of the multilingual approach (1) and also that it outperforms the monolingual models on some monolingual datasets.